Project Xarvis

Xpanvictor

Xarvis is an AI-powered virtual assistant inspired by Iron Man's Jarvis, designed to execute commands, control devices, manage tasks, and integrate with various systems. It supports speech-to-text, text-to-speech, and NLP-based command execution, with a modular architecture for flexibility and scalability.

Astounded, life carries me, left with no remorse, I can only hope to breath while I await death. Halo, Xarvis to save as much as possible, time flees.

Table of Contents

1. Project Overview
2. System Architecture
3. Features
4. Backbone
5. Core Components
6. Setup
7. Usage
8. Contributing
9. Licence

1. Project Overview

Xarvis is a modular AI system designed to automate tasks, control devices, and assist in daily activities. It integrates natural language processing (NLP), speech recognition, and text-to-speech functionalities, while also serving as a platform for automation, device control, and task management. Xarvis's architecture is designed to be flexible, scalable, and adaptable for future functionalities.

2. System Architecture

Xarvis is composed of two key layers:

• Backbone:

  1. General Proxy and Pipeline Server
  2. Job Runner
  3. Aggregator
  4. Brain (Intensive Calculation and Code Generation)

• Core Components:

  1. Natural Language Processing (NLP)
  2. Speech Recognition
  3. Text-to-Speech (TTS)
  4. Internal Server System (ISS)
  5. Command Execution Layer (CEL)
  6. Integration Layer (IL)
     • Visual Layer
  7. Automations

3. Features

• Voice Interaction: Speech recognition and text-to-speech modules for interacting with the system via voice commands.
• NLP Command Parsing: NLP engine for understanding commands and executing corresponding actions.
• Device Control: Integration with ESP32-CAM and other devices for hardware interaction.
• Automation: Define custom automations for task scheduling and reminders.
• Modular Design: Easy to add new features and expand functionality.
• Custom Command Execution: Ability to run system commands and scripts based on voice input.

4. Backbone

• General Proxy and Pipeline Server: A server that handles communication between different modules and the external world.
• Job Runner: Manages tasks and processes commands in a queue.
• Aggregator: Collects information from various sources, integrates data, and passes it to the Brain.
• Brain (Intensive Calculation and Code Generation): The core computational unit responsible for decision-making, complex calculations, and generating code where necessary.

5. Core Components

• Natural Language Processing (NLP): This module processes user commands and converts them into actionable tasks.
• Speech Recognition: Converts voice input into text using STT (Speech-to-Text) technology.
• Text-to-Speech (TTS): Converts system responses or generated text into spoken output.
• Internal Server System (ISS): Manages server requests and communication between modules.
• Command Execution Layer (CEL): Executes system commands or predefined tasks based on parsed NLP results.
• Integration Layer (IL): Includes both a visual layer for feedback and automation handling for system integration.
• Automations: Users can schedule tasks, set reminders, and automate specific activities.

6. Setup

Requirements:

• Python 3.x
• ESP32-CAM module
• Microphone (for voice input)
• Bluetooth devices (for TTS playback)

Install Dependencies:

Clone the repository:

git clone https://github.com/your-repo/xarvis.git
cd xarvis
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

7. Usage

python main.py

Could also use make.

make init
make test
make run

8. Contributing

Feel free to send PRs.

9. Licence

Refer to LICENCE.